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TU Berlin

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Neural Information Processing Group

We are concerned with the principles underlying information processing in biological systems. On the one hand we want to understand how the brain computes, on the other hand we want to utilize the strategies employed by biological systems for machine learning applications. Our research interests cover three thematic areas.

Models of Neuronal Systems:

Lupe

In collaboration with neurobiologists and clinicians we study how the visual system processes visual information. Research topics include: cortical dynamics, the representation of visual information, adaptation and plasticity, and the role of feedback. More recently we became interested in how perception is linked to cognitive function, and we began to study computational models of decision making in uncertain environments, and how those processes interact with perception and memory.

Machine Learning and Neural Networks:

Lupe

Here we investigate how machines can learn from examples in order to predict and (more recently) act. Research topics include the learning of proper representations, active and semisupervised learning schemes, and prototype-based methods. Motivated by the model-based analysis of decision making in humans we also became interested in reinforcement learning schemes and how these methods can be extended to cope with multi-objective cost functions. In collaboration with colleagues from the application domains, machine learning methods are applied to different problems ranging from computer vision, information retrieval, to chemoinformatics.

Analysis of Neural Data:

Lupe

Here we are interested to apply machine learning and statistical methods to the analysis of multivariate biomedical data, in particular to data which form the basis of our computational studies of neural systems. Research topics vary and currently include spike-sorting and the analysis of multi-tetrode recordings, confocal microscopy and 3D-reconstruction techniques, and the analysis of imaging data. Recently we became interested in the analysis of multimodal data, for example, correlating anatomical, imaging, and genetic data.

Selected Publications

Franke, F., Natora, M., Boucsein, C., Munk, M. and Obermayer, K. (2010). An Online Spike Detection and Spike Classification Algorithm Capable of Instantaneous Resolution of Overlapping Spikes. Journal of Computional Neuroscience, 127 – 148.


Jain, B. and Obermayer, K. (2009). Structure Spaces. Journal of Machine Learning Research, 10, 2667 – 2714.


Stimberg, M., Wimmer, K., Martin, R., Schwabe, L., Marino, J., Schummers, J., Lyon, D., Sur, M. and Obermayer, K. (2009). The Operating Regime of Local Computations in Primary Visual Cortex. Cerebral Cortex, 19, 2166 – 2180.


Henrich, F. and Obermayer, K. (2008). Active Learning by Spherical Subdivision. Journal of Machine Learning Research, 9, 105 – 130.


Young, J., Waleszczyk, W., Wang, C., Calford, M., Dreher, B. and Obermayer, K. (2007). Cortical Reorganization Consistent with Spike Timing- but not Correlation-Dependent Plasticity. Nat. Neurosci., 10, 887 – 889.


Hochreiter, J. and Obermayer, K. (2006). Support Vector Machines for Dyadic Data. Neural Comput., 18, 1472 – 1510.


Mariño, J., Schummers, J., Lyon, D., Schwabe, L., Beck, O., Wiesing, P., Obermayer, K. and Sur, M. (2005). Invariant Computations in Local Cortical Networks with Balanced Excitation and Inhibition. Nature Neuroscience, 8, 194 – 201.


Schmitt, S., Evers, J.-F., Duch, C., Scholz, M. and Obermayer, K. (2004). New Methods for the Computer-Assisted 3D Reconstruction of Neurons from Confocal Image Stacks. Neuroimage, 23, 1283 – 1298.


Wenning, G. and Obermayer, K. (2003). Activity Driven Adaptive Stochastic Resonance. Physical Review Letters, 90, 120602.


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